Outliers can be discovered using higher-order statistics, when the mean and covariance information are not sufficient [1]}, [2]}.
In hyperspectral imaging (HSI) [3]}, the detection goal is to find a low-rank feature subspace, denoted by \(\mathbf {Q}\in \operatorname{St}(n,r)\) .
We wish that after transforming HSI pixels by \(\mathbf {Q},\)  unusual targets in the image are revealed [4]}.
Intuitively if the anomalies are captured by \(\text{colspan}(\mathbf {Q})\) , they would stand out as bright spots in the transformed image.
